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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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COFFEE: consensus single cell-type specific inference for gene regulatory networks.

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  • 1Integrative Life Sciences, Virginia Commonwealth University, 1000 W Cary St, Richmond, VA 23284, United States.

Briefings in Bioinformatics
|September 23, 2024
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Summary

COFFEE, a consensus algorithm, improves gene regulatory network (GRN) inference for single-cell RNA sequencing (scRNA-seq) data by integrating multiple methods. This approach enhances accuracy across various datasets, offering a flexible strategy for biological research.

Keywords:
gene regulatory networkssingle-cell biologytranscriptional mechanismswisdom-of-crowds

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) are essential for understanding biological processes.
  • Computational inference of GRNs from scRNA-seq data is a key challenge.
  • Integrating multiple GRN inference methods can improve performance.

Purpose of the Study:

  • To develop a consensus algorithm for cell-type specific GRN inference from scRNA-seq data.
  • To evaluate the performance of the consensus algorithm against baseline methods.
  • To demonstrate the flexibility and applicability of the consensus approach.

Main Methods:

  • Developed COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm.
  • Integrated information from 10 established GRN inference methods.
  • Benchmarked COFFEE on synthetic, curated, and experimental scRNA-seq datasets.

Main Results:

  • COFFEE demonstrated improved performance compared to individual baseline GRN inference methods.
  • A modified version of COFFEE enhanced the performance of newer cell-type specific GRN inference methods.
  • The consensus approach proved valuable for GRN inference at the single-cell level.

Conclusions:

  • Consensus-based methods, like COFFEE, are effective for GRN inference in scRNA-seq data.
  • COFFEE offers a flexible framework adaptable to various GRN inference algorithms.
  • The study highlights the continued importance of ensemble strategies in computational biology.